A Quaternion-Embedded Capsule Network Model for Knowledge Graph Completion

Knowledge graphs are collections of factual triples. Link prediction aims to predict lost factual triples in knowledge graphs. In this paper, we present a novel capsule network method for link prediction taking advantages of quaternion. More specifically, we explore two methods, including a relational rotation model called QuaR and a deep capsule neural model called CapS-QuaR to encode semantics of factual triples. QuaR model defines each relation as a rotation from the head entity to the tail entity in the hyper-complex vector space, which could be used to infer and model diverse relation patterns, including: symmetry/anti-symmetry, reversal and combination. Based on these characteristics of quaternions, we use the embeddings of entities and relations trained from QuaR as the input to CapS-QuaR model. Experimental results on multiple benchmark knowledge graphs show that the proposed method is not only scalable, but also able to predict the correctness of triples in knowledge graphs and significantly outperform the existing state-of-the-art models for link prediction. Finally, the evaluation of a real dataset for search personalization task is conducted to prove the effectiveness of our model.